Extracranial Cerebrovascular Occlusive Disease

Harrigan, Mark R.; Deveikis, John P.

doi:10.1007/978-3-319-66779-9_18

Cited by 2 publications

(2 citation statements)

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“…Manual labeling of 42 arterial segments was used as the reference standard to develop the algorithm and evaluate the performance of the proposed model. Trained annotators labeled 42 arterial segments according to the anatomical segments of cerebral vessels ( Bouthillier, van Loveren, and Keller, 1996 ; Yavagal and Haussen, 2011 ; Harrigan and Deveikis, 2018 ): including 7 segments of the ICA (C1-C7), 3 segments of the middle cerebral artery (MCA: M1, M2, M3-4), 4 segments of the anterior cerebral artery (ACA: A1, A2, A3, and A4-5), 3 segments of the posterior cerebral artery (PCA: P1, P2, and P3-4), 2 segments of the intracranial vertebral artery (VA: V3 and V4), as well as the posterior communicating artery (PCoA), anterior communicating artery (ACoA), and the basilar artery (BA).…”

Section: Methodsmentioning

confidence: 99%

“…Hence, precise anatomical labeling of the intracranial arteries is crucial for physicians to understanding the mechanism, diagnosis, and treatment of cerebrovascular conditions. Although consensus on defining the fine segments of intracranial arteries through imaging and anatomy has been established ( Bouthillier, van Loveren, and Keller, 1996 ; Yavagal and Haussen, 2011 ; Harrigan and Deveikis, 2018 ), manual labeling of fine segments of intracranial arteries is time-consuming and prone to inter-and intra-observer variability. Furthermore, this problem is exacerbated due to the lack of experienced radiologists given the increasing imaging data.…”

Section: Introductionmentioning

confidence: 99%

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Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography

Chen,

You,

Zhang

et al. 2024

Front. Physiol.

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Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs).Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model’s potential in enhancing clinicians’ precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss’ Kappa) were calculated to assess the differences in clinical performance of clinicians.Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians’ mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss’ Kappa) increased by 0.07 (p = 0.029).Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

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Section: Methodsmentioning

confidence: 99%